Overview

Dataset statistics

Number of variables35
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory402.1 KiB
Average record size in memory280.1 B

Variable types

Numeric15
Categorical18
Boolean2

Alerts

EmployeeCount has constant value "1" Constant
Over18 has constant value "True" Constant
StandardHours has constant value "80" Constant
Age is highly correlated with TotalWorkingYearsHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
Age is highly correlated with JobLevel and 1 other fieldsHigh correlation
JobLevel is highly correlated with Age and 3 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 2 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly correlated with JobLevel and 5 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 2 other fieldsHigh correlation
Age is highly correlated with TotalWorkingYearsHigh correlation
JobLevel is highly correlated with MonthlyIncome and 1 other fieldsHigh correlation
MonthlyIncome is highly correlated with JobLevel and 1 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly correlated with Age and 3 other fieldsHigh correlation
YearsAtCompany is highly correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with YearsAtCompany and 1 other fieldsHigh correlation
Department is highly correlated with StandardHours and 4 other fieldsHigh correlation
StandardHours is highly correlated with Department and 18 other fieldsHigh correlation
StockOptionLevel is highly correlated with StandardHours and 3 other fieldsHigh correlation
Attrition is highly correlated with StandardHours and 2 other fieldsHigh correlation
JobLevel is highly correlated with StandardHours and 3 other fieldsHigh correlation
JobInvolvement is highly correlated with StandardHours and 2 other fieldsHigh correlation
RelationshipSatisfaction is highly correlated with StandardHours and 2 other fieldsHigh correlation
Education is highly correlated with StandardHours and 2 other fieldsHigh correlation
EmployeeCount is highly correlated with Department and 18 other fieldsHigh correlation
WorkLifeBalance is highly correlated with StandardHours and 2 other fieldsHigh correlation
EducationField is highly correlated with Department and 3 other fieldsHigh correlation
EnvironmentSatisfaction is highly correlated with StandardHours and 2 other fieldsHigh correlation
OverTime is highly correlated with StandardHours and 2 other fieldsHigh correlation
MaritalStatus is highly correlated with StandardHours and 3 other fieldsHigh correlation
Over18 is highly correlated with Department and 18 other fieldsHigh correlation
BusinessTravel is highly correlated with StandardHours and 2 other fieldsHigh correlation
PerformanceRating is highly correlated with StandardHours and 2 other fieldsHigh correlation
JobSatisfaction is highly correlated with StandardHours and 2 other fieldsHigh correlation
JobRole is highly correlated with Department and 4 other fieldsHigh correlation
Gender is highly correlated with StandardHours and 2 other fieldsHigh correlation
Age is highly correlated with JobLevel and 3 other fieldsHigh correlation
Department is highly correlated with EducationField and 1 other fieldsHigh correlation
EducationField is highly correlated with Department and 1 other fieldsHigh correlation
JobLevel is highly correlated with Age and 6 other fieldsHigh correlation
JobRole is highly correlated with Department and 4 other fieldsHigh correlation
MaritalStatus is highly correlated with StockOptionLevelHigh correlation
MonthlyIncome is highly correlated with Age and 4 other fieldsHigh correlation
PercentSalaryHike is highly correlated with PerformanceRatingHigh correlation
PerformanceRating is highly correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly correlated with MaritalStatusHigh correlation
TotalWorkingYears is highly correlated with Age and 7 other fieldsHigh correlation
YearsAtCompany is highly correlated with Age and 6 other fieldsHigh correlation
YearsInCurrentRole is highly correlated with JobLevel and 4 other fieldsHigh correlation
YearsSinceLastPromotion is highly correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsWithCurrManager is highly correlated with JobLevel and 4 other fieldsHigh correlation
EmployeeNumber has unique values Unique
NumCompaniesWorked has 197 (13.4%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2021-12-06 21:49:51.250254
Analysis finished2021-12-06 21:50:24.002787
Duration32.75 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:24.087531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
MonotonicityNot monotonic
2021-12-07T03:20:24.171221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3858
 
3.9%
3358
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Attrition
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1233 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Length

2021-12-07T03:20:24.421972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:24.484500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BusinessTravel
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Length

Max length17
Median length13
Mean length13.44761905
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely1043
71.0%
Travel_Frequently277
 
18.8%
Non-Travel150
 
10.2%

Length

2021-12-07T03:20:24.547609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:24.616863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely1043
71.0%
travel_frequently277
 
18.8%
non-travel150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:24.688931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5090999
Coefficient of variation (CV)0.5028240288
Kurtosis-1.203822808
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003518568352
Sum1179654
Variance162819.5937
MonotonicityNot monotonic
2021-12-07T03:20:24.782805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6916
 
0.4%
10825
 
0.3%
4085
 
0.3%
3295
 
0.3%
5305
 
0.3%
13295
 
0.3%
4274
 
0.3%
14694
 
0.3%
1474
 
0.3%
12254
 
0.3%
Other values (876)1423
96.8%
ValueCountFrequency (%)
1021
 
0.1%
1031
 
0.1%
1041
 
0.1%
1051
 
0.1%
1061
 
0.1%
1071
 
0.1%
1091
 
0.1%
1113
0.2%
1151
 
0.1%
1162
0.1%
ValueCountFrequency (%)
14991
 
0.1%
14981
 
0.1%
14962
0.1%
14953
0.2%
14921
 
0.1%
14904
0.3%
14881
 
0.1%
14853
0.2%
14821
 
0.1%
14802
0.1%

Department
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.54217687
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development961
65.4%
Sales446
30.3%
Human Resources63
 
4.3%

Length

2021-12-07T03:20:24.908783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:24.971741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
research961
27.8%
961
27.8%
development961
27.8%
sales446
12.9%
human63
 
1.8%
resources63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:25.035079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
MonotonicityNot monotonic
2021-12-07T03:20:25.129056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

Education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2021-12-07T03:20:25.223256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:25.285712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EducationField
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Life Sciences
606 
Medical
464 
Marketing
159 
Technical Degree
132 
Other
82 

Length

Max length16
Median length13
Mean length10.53333333
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences606
41.2%
Medical464
31.6%
Marketing159
 
10.8%
Technical Degree132
 
9.0%
Other82
 
5.6%
Human Resources27
 
1.8%

Length

2021-12-07T03:20:25.379911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:25.447452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
life606
27.1%
sciences606
27.1%
medical464
20.8%
marketing159
 
7.1%
technical132
 
5.9%
degree132
 
5.9%
other82
 
3.7%
human27
 
1.2%
resources27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EmployeeCount
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11470
100.0%

Length

2021-12-07T03:20:25.554105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:25.618430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
11470
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EmployeeNumber
Real number (ℝ≥0)

UNIQUE

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.865306
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:25.679851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.0243348
Coefficient of variation (CV)0.5874180063
Kurtosis-1.223178906
Mean1024.865306
Median Absolute Deviation (MAD)533.5
Skewness0.01657401958
Sum1506552
Variance362433.2997
MonotonicityStrictly increasing
2021-12-07T03:20:25.773482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20481
 
0.1%
13681
 
0.1%
13641
 
0.1%
13631
 
0.1%
13621
 
0.1%
13611
 
0.1%
13601
 
0.1%
13581
 
0.1%
13561
 
0.1%
13551
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

EnvironmentSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Length

2021-12-07T03:20:25.883336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:25.946302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Male
882 
Female
588 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male882
60.0%
Female588
40.0%

Length

2021-12-07T03:20:26.024849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:26.087380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male882
60.0%
female588
40.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:26.165938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.32942759
Coefficient of variation (CV)0.3085304415
Kurtosis-1.196398456
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.0323109529
Sum96860
Variance413.2856263
MonotonicityNot monotonic
2021-12-07T03:20:26.260360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6629
 
2.0%
4228
 
1.9%
9828
 
1.9%
8428
 
1.9%
4828
 
1.9%
9627
 
1.8%
7927
 
1.8%
5727
 
1.8%
8726
 
1.8%
5626
 
1.8%
Other values (61)1196
81.4%
ValueCountFrequency (%)
3019
1.3%
3115
1.0%
3224
1.6%
3319
1.3%
3412
0.8%
3518
1.2%
3618
1.2%
3718
1.2%
3813
0.9%
3917
1.2%
ValueCountFrequency (%)
10019
1.3%
9920
1.4%
9828
1.9%
9721
1.4%
9627
1.8%
9523
1.6%
9422
1.5%
9316
1.1%
9225
1.7%
9118
1.2%

JobInvolvement
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2021-12-07T03:20:26.370171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:26.574450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Length

2021-12-07T03:20:26.653028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:26.720574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobRole
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length18
Mean length18.0707483
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive326
22.2%
Research Scientist292
19.9%
Laboratory Technician259
17.6%
Manufacturing Director145
9.9%
Healthcare Representative131
8.9%
Manager102
 
6.9%
Sales Representative83
 
5.6%
Research Director80
 
5.4%
Human Resources52
 
3.5%

Length

2021-12-07T03:20:26.820898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:26.888456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sales409
14.4%
research372
13.1%
executive326
11.5%
scientist292
10.3%
laboratory259
9.1%
technician259
9.1%
director225
7.9%
representative214
7.5%
manufacturing145
 
5.1%
healthcare131
 
4.6%
Other values (3)206
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

JobSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2021-12-07T03:20:27.029905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:27.092417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MaritalStatus
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Married
673 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.902721088
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married673
45.8%
Single470
32.0%
Divorced327
22.2%

Length

2021-12-07T03:20:27.170936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:27.249466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
married673
45.8%
single470
32.0%
divorced327
22.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthlyIncome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:27.328070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
MonotonicityNot monotonic
2021-12-07T03:20:27.437843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
26103
 
0.2%
25593
 
0.2%
63473
 
0.2%
24043
 
0.2%
34523
 
0.2%
55623
 
0.2%
24513
 
0.2%
27413
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:27.547625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786044
Coefficient of variation (CV)0.4972915967
Kurtosis-1.2149561
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01857780789
Sum21040262
Variance50662878.17
MonotonicityNot monotonic
2021-12-07T03:20:27.657407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42233
 
0.2%
91503
 
0.2%
90962
 
0.1%
130082
 
0.1%
128582
 
0.1%
68812
 
0.1%
230162
 
0.1%
89522
 
0.1%
203642
 
0.1%
221022
 
0.1%
Other values (1417)1448
98.5%
ValueCountFrequency (%)
20941
0.1%
20971
0.1%
21041
0.1%
21121
0.1%
21221
0.1%
21252
0.1%
21371
0.1%
22271
0.1%
22431
0.1%
22531
0.1%
ValueCountFrequency (%)
269991
0.1%
269971
0.1%
269681
0.1%
269591
0.1%
269561
0.1%
269331
0.1%
269141
0.1%
268971
0.1%
268941
0.1%
268621
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:27.751560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
MonotonicityNot monotonic
2021-12-07T03:20:27.823069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

Over18
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1470 
ValueCountFrequency (%)
True1470
100.0%
2021-12-07T03:20:27.876979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

OverTime
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False1054
71.7%
True416
 
28.3%
2021-12-07T03:20:27.908231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:27.955553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659937717
Coefficient of variation (CV)0.2406346025
Kurtosis-0.3005982221
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8211279756
Sum22358
Variance13.39514409
MonotonicityNot monotonic
2021-12-07T03:20:28.034106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

PerformanceRating
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Length

2021-12-07T03:20:28.128512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:28.191285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RelationshipSatisfaction
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Length

2021-12-07T03:20:28.254207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:28.316730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

StandardHours
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
80
1470 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
801470
100.0%

Length

2021-12-07T03:20:28.395310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:28.442761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
801470
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

StockOptionLevel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Length

2021-12-07T03:20:28.505265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:28.568443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotalWorkingYears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:28.646803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
MonotonicityNot monotonic
2021-12-07T03:20:28.897205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:28.991423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289270621
Coefficient of variation (CV)0.4605656896
Kurtosis0.494992986
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5531241711
Sum4115
Variance1.662218734
MonotonicityNot monotonic
2021-12-07T03:20:29.054381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

WorkLifeBalance
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Length

2021-12-07T03:20:29.148943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-07T03:20:29.211448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

YearsAtCompany
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:29.290027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
MonotonicityNot monotonic
2021-12-07T03:20:29.384233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:29.478436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137035
Coefficient of variation (CV)0.856685128
Kurtosis0.4774207735
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9173631563
Sum6217
Variance13.12712197
MonotonicityNot monotonic
2021-12-07T03:20:29.572419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:29.666598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
MonotonicityNot monotonic
2021-12-07T03:20:29.745179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-12-07T03:20:29.827339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
MonotonicityNot monotonic
2021-12-07T03:20:29.917518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

Interactions

2021-12-07T03:20:20.647317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:56.419442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:58.059824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:59.703019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:01.491234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:03.036376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:04.845425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:06.542837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:08.241878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:09.794923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:11.450436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:13.307209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:14.964722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:17.024647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:18.816246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:20.755728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:56.526947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:58.172208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:59.790411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:01.584938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:03.127463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:04.942424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:06.639156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:08.338614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:09.891731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:11.551613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:13.408767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:15.088048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:17.142979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:18.917786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:20.871487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:56.628980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:58.278426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:59.911537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:01.678142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:03.242249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:05.058716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:06.745375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:08.442687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:10.002594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:11.666023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:13.517263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:15.213569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:17.258896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:19.029619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:20.983566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:56.727033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:58.386018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:00.018383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:01.779737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:03.357751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:05.173917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:06.842381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:08.544464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:10.112591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:11.782570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:13.624412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:15.329305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:17.374473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:19.132203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:21.086117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:56.925911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:58.486677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:00.113553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:01.883484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:03.458901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:05.276471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:06.945548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:08.632824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:10.210550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:12.022836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:13.728103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:15.453297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:17.496349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:19.247683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:20:21.201844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:57.031896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-07T03:19:58.597087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-12-07T03:20:30.043670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-07T03:20:30.373121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-07T03:20:30.706348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-07T03:20:31.029758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-07T03:20:31.292689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-07T03:20:22.491353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-07T03:20:23.791521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
0411Travel_Rarely1102Sales12Life Sciences112Female9432Sales Executive4Single5993194798YYes11318008016405
1490Travel_Frequently279Research & Development81Life Sciences123Male6122Research Scientist2Married5130249071YNo2344801103310717
2371Travel_Rarely1373Research & Development22Other144Male9221Laboratory Technician3Single209023966YYes15328007330000
3330Travel_Frequently1392Research & Development34Life Sciences154Female5631Research Scientist3Married2909231591YYes11338008338730
4270Travel_Rarely591Research & Development21Medical171Male4031Laboratory Technician2Married3468166329YNo12348016332222
5320Travel_Frequently1005Research & Development22Life Sciences184Male7931Laboratory Technician4Single3068118640YNo13338008227736
6590Travel_Rarely1324Research & Development33Medical1103Female8141Laboratory Technician1Married267099644YYes204180312321000
7300Travel_Rarely1358Research & Development241Life Sciences1114Male6731Laboratory Technician3Divorced2693133351YNo22428011231000
8380Travel_Frequently216Research & Development233Life Sciences1124Male4423Manufacturing Director3Single952687870YNo214280010239718
9360Travel_Rarely1299Research & Development273Medical1133Male9432Healthcare Representative3Married5237165776YNo133280217327777

Last rows

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
1460290Travel_Rarely468Research & Development284Medical120544Female7321Research Scientist1Single378584891YNo14328005315404
1461501Travel_Rarely410Sales283Marketing120554Male3923Sales Executive1Divorced10854165864YYes133280120333220
1462390Travel_Rarely722Sales241Marketing120562Female6024Sales Executive4Married1203188280YNo1131801212220996
1463310Non-Travel325Research & Development53Medical120572Male7432Manufacturing Director1Single993637870YNo193280010239417
1464260Travel_Rarely1167Sales53Other120604Female3021Sales Representative3Single2966213780YNo18348005234200
1465360Travel_Frequently884Research & Development232Medical120613Male4142Laboratory Technician4Married2571122904YNo173380117335203
1466390Travel_Rarely613Research & Development61Medical120624Male4223Healthcare Representative1Married9991214574YNo15318019537717
1467270Travel_Rarely155Research & Development43Life Sciences120642Male8742Manufacturing Director2Married614251741YYes20428016036203
1468490Travel_Frequently1023Sales23Medical120654Male6322Sales Executive2Married5390132432YNo143480017329608
1469340Travel_Rarely628Research & Development83Medical120682Male8242Laboratory Technician3Married4404102282YNo12318006344312